Category: Air pollution exposure

Huang et al.: Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020.

Wenhao Wang May 17, 2023 0 Comments
Keyong Huang, Qingyang Zhu, Xiangfeng Lu, Dongfeng Gu, Yang Liu. (2023). Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020. GeoHealth, 2023, 7(5): e2023GH000798. doi: 10.1029/2023GH000798.   Read online:...

Meng and Hang et al.: A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Yun Hang January 18, 2023 0 Comments
Meng, X., Hang, Y., Lin, X., Li, T., Wang, T., Cao, J., Fu, Q., Dey, S., Huang, K., Liang, F. and Kan, H., 2023. A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China. Environment International, p.107740. Science Direct: Link Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge...

Vu et al.: Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Bryan March 14, 2022 0 Comments
Vu, B.N., Bi, J., Wang, W., Huff, A., Kondragunta, S., Liu, Y. (2022). Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California. Remote Sensing of Environment, 271, 112890. Science Direct: Link Particulate matter from wildland fire smoke can traverse hundreds of kilometers from where they originated...

Bi et al.: Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Yang Liu PhD January 13, 2022 0 Comments
Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA’s Goddard Earth Observing System “Composition...

Wang et al.: A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Wenhao Wang October 5, 2021 0 Comments
Wenhao Wang, Xiong Liu, Jianzhao Bi, Yang Liu, A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology, Environment International, Volume 158, 2022, 106917https://doi.org/10.1016/j.envint.2021.106917 Abstract: Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone...

Zhang et al.: A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Danlu Zhang September 29, 2021 0 Comments
Danlu Zhang, Linlin Du, Wenhao Wang, Qingyang Zhu, Jianzhao Bi, Noah Scovronick, Mogesh Naidoo, Rebecca M. Garland, Yang Liu. (2021). A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa. Remote Sensing of Environment, 266, 112713. Elsevier: Link Exposure to fine particulate matter (PM2.5) has been linked to a substantial...

Wang et al.: Satellite-based assessment of the long-term efficacy of PM2.5 pollution control policies across the Taiwan Strait

Qiulun Li September 17, 2020 0 Comments
Evaluating the efficacy of air pollution control policies is an essential part of the decision-making process to develop new policies and improve existing measures.  In this analysis, we assessed the effects air pollution control policies in the Taiwan Strait Region from 2005 to 2018 using full-coverage, high-resolution PM2.5generated by a satellite-driven machine learning model. A ten-fold...

Stowell et. al: Estimating PM2.5 in Southern California using satellite data: factors that affect model performance.

Qiulun Li May 18, 2020 0 Comments
In the article, the authors focus on a region where traditional satellite AOD models have not performed as well compared to other areas of the US, in order to determine which region-specific parameters have the highest impact on model accuracy. Using a two-stage linear approach, the authors identified important meteorological and land use parameters including temperature, relative humidity,...

Geng et al.: Random forest models for PM2.5 speciation using MISR data

Yang Liu PhD April 9, 2020 0 Comments
Random forest models were developed to predict ground-level daily PM2.5 speciation concentrations in California from MISR fractional AODs and other supporting data such as ground measurements, chemical transport model simulations, land use variables and meteorological fields. Sensitivity tests were also conducted to explore the influence of variable selection on model performance. Results shows...

She et al.: Hourly PM2.5 levels during heavy winter episodes in the Yangtze River Delta

Yang Liu PhD October 6, 2019 0 Comments
In this publication, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding...